{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ch 8.3 AdaBoost \n",
    "从网上下载或自己编程实现AdaBoost，以不剪枝决策树为基学习器，在西瓜数据集3.0α上训练一个AdaBoost集成"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dataset = pd.read_csv('data/table_4_3_watermelon_3_0_num.csv')\n",
    "dataset = dataset[['density','sugar_ratio','label']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# create base learner\n",
    "def train_adboost(dataset, num_base):\n",
    "    base_learner = DecisionTreeClassifier()\n",
    "    ada_boost = AdaBoostClassifier(base_learner, n_estimators=num_base)\n",
    "    ada_boost.fit(dataset.ix[:,[0,1]], dataset.ix[:,2])\n",
    "    return ada_boost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def print_error_weight(ada_boost):\n",
    "    print('Base Learner error and weight:')\n",
    "    for idx, err, weight in zip(range(1, 11), ada_boost.estimator_errors_, ada_boost.estimator_weights_):\n",
    "        print('Base Learner-%d\\t' % idx, err, '\\t', weight)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def plot_boundary(dataset,ada_boost, plot_num, num_base):\n",
    "    fig = plt.subplot(plot_num)\n",
    "    test_density = np.array(dataset['density'])\n",
    "    test_sugar_ratio = np.array(dataset['sugar_ratio'])\n",
    "    x_step = (max(test_density) - min(test_density))/20\n",
    "    y_step = (max(test_sugar_ratio) - min(test_sugar_ratio))/20\n",
    "    test_x = np.arange(min(test_density)-5*x_step, max(test_density)+5*x_step, x_step)\n",
    "    test_y = np.arange(min(test_sugar_ratio)-5*y_step, max(test_sugar_ratio)+5*y_step, y_step)\n",
    "    test_data = np.transpose([np.tile(test_x, len(test_y)), np.repeat(test_y, len(test_x))])\n",
    "    test_out = ada_boost.predict(test_data)\n",
    "    for i in range(len(test_out)):\n",
    "        if test_out[i]:\n",
    "            plt.plot(test_data[i,0], test_data[i,1], 'bx', alpha=0.5)\n",
    "        else:\n",
    "            plt.plot(test_data[i,0], test_data[i,1], 'gx', alpha=0.5)\n",
    "    dataset0 = dataset[dataset['label']==0]\n",
    "    dataset1 = dataset[dataset['label']==1]\n",
    "    plt.plot(list(dataset0['density']), list(dataset0['sugar_ratio']), 'g.')\n",
    "    plt.plot(list(dataset1['density']), list(dataset1['sugar_ratio']), 'b.')\n",
    "    plt.title('num_classifier='+str(num_base))\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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3m8crVy0yj1d+9plJRtXYCJ98YjrLgTdBrl1rMjKUl8Pbb5vfYtUqrvGbOjW0TXk5vLq1\nmbden4XfL6SlwX9+4gx3fbWNxsOZTNVi47dvl/FbeIfhlkaKb7k4yObVPa9xuu+3PFLyBO37bo/Y\nz45NNLk92aeoP97Bt75xa/B1GNVVpCNmpR6CtcC7oQ4Ml5B3WcmdbN8O1QfN45XVB+voOV5Gz/Gy\n/rLevl62/IwB6RHCxq5fVLmvfE7PsbKQTfiDD5oWQMT8vXz5+mVFVqaH+//sX7jr3h5efBGyvZ+z\n5cBPB/x2XqAn5wN6cj6geueFAe4gm007q3mz90/YdeU11n98H96FH0XkZ9cmmtzbj25n2VdGaFKH\nQVRHh0TkXkwluCvUcVV9DSOVKCsrG9wEZTVBaRXUV8LkIqgvhEXm+QLq15my0x7krufh9LPQfE9o\nG7t+0eQu/ldoqAS+EPTbmL+lpeYleEeOmAmr/GHbywib/oszyZ0whVvW/SOlZXM5uL0VefNZeKga\nCk9A+xGoXWds79wKhXNh5mCbk4dfwX/Oj4ri43P2yyamlZSN6BcJd0gbu36hbBoqoewLkB3BbxUC\n0cpKjYjcCvwEeEBVz482kB27Wlgxq5KKJUWm17+kiIzORWR0Luovm3iDh9UFzzLxBk9YG7t+UeU+\nvYqM/MawIxpr1phHAYuLozQ6VHCYjCmfU1E214xYjU9n9VOnmDg+3YxYlc0lo6CBjIKGsDaVi+7D\nI+MQ0kj3jMev/oj87NpEk3vFA93s+O0no73kBq7dCPoE44AjwH2Yi78OeFRV64NsCoD3gcdVdVck\nX3zNk2VN8Kp1kx2rJ62Sgbu6Gjr9p+C2H5qy63iS7nD7YVovtTJ32lzWLFzjjKf0IrABWLdoHUXZ\nRcHXbfSeMVbVPhEJZKX2AJvUykptHX8F+C4wDfNeAoC+SAMYLZw46hBvbjx9oGnAwHqlSEfDznad\n5WcHfsblvsvkTs4ld3IuWROyRvSLhNsOYskdls8pzxi//LLRyz7fwKiH3dESO36Jyl1eDm+feR0+\nLWXVg71mxOryZxRnFdPY2cjUCVPNiNWRt0Fh1c2r+m0E4aW6l/D5faR70vn6LV8nb3Jev004v0i4\nR7KJJrdHPNS31fOtxd/qv54S8hnjZcvMpIf7pJWZTDtzJrJJr+pq6Pnka/QU/GLUk4NVDVX4/D4U\n5YrvCk0dTc6beIzAZvux7SybY3+2LAHXDsXOzwncZ8/Ctm0D8wg33xyBk005lHlDJq1drShKmqQN\nTi8/DJJNDjmmJdixw6wBSfUHz9vbB7/hpbg4Ar/S98k49Z9GvVbqqUVP8Y153+DLhV/mxYoXKc4u\ndt46rAhsVsxZwY5j9hcPOaYSuHLI7Hu9g9/wcvx47OTQlgNbyJ6YzXfu/g6nL5525josVw5dCydI\nllhy5+aaybQJE6CtbWDN0bC4jtEhO0hU7rB8Thkdqq2FvDzz7Gtg1CPeKb8Tgbu8HNozauFSHt7Z\nzfFJRR9n7sKsQloutXBnwUAy0sTMRTqzllMXCqirm0l5uZWMdcE2AJrq7qe8HKrea4YbOznUmkXl\nVwtD2tj1S1RuU9YJ2R/T1NIdn1T0ceb2iAdP2nXkZo80fXW0t2tSs+87qcu/9e9as+9k//6Sh9/X\nJQ+/31+28Y196l3wG934xr6wNnb9RsNd8uRL+odb/0J3ndwVdW5bfute1yUbl49pKvr1767Xv9r5\nV/rD//hh3FPoB6f0D4CETM1eC75Jp9jd/X8H0qAffQIUuuf+H5MGfUcL875QyqEL9VQuywtpY9cv\nUu5zB0t56dxD9OnnpHvSWZH5LPfe9PWocNv2uzwHLuXRnbNzTFLRT/BM4KXfvIRf/XjSPHz7tm9T\nnF0ctxT6S/OX4knzJIccmjklD1/n4gHNmLUTAG+GKZv3u9NYcNNFOHeBmhNHQ9rY9YuU+8dnf4zv\n7JX+Caar3r0sWPKVqHDb9utph9xTA37XmYr+XPc5nvvgOR685UFunHTjNX4HPj2AT80kGwqtXa0s\nnbE0bin0Z06dScsl+7nZHdMSJEpq9sPth3nnyDv41U+6J51lc5bReqk1bgvKon2+Z7vOsu3oNvx+\n/6B0lcF+O0/s5Mj5I/2TbKFskmoBndMQ76G33Mm5rJy7ko7LHVSWVHLw04Oc7Rp5HDOWcdv1C2XT\neqm1fymF+pXWS63X5Gwd00RmNm1GA8dMliVStonirGI2lG+gvac97jOo0T7foqwi0j3pCEK6J52i\n7KL4JjJLqRnjZMo2MYbc0Y4pa2IWlSWVrF24lsqSSrImZDnqfGMxY+yYShAprreZrW+r56PTH4WU\nME5swuMRU35mPo/Of5T8zNDPfsb7fF05dB3NbEdvB+ur1/PhiQ/ZdnQbnb2djpNa8ZZDicjtyqFR\nNLPNnc34/eYdBT6/j6bOpqSXQ94MLxtqNtDY0Rh3qeVUOZRSo0O5k3MZ7xnPFd+VkOvnndiEX4/f\n2a6zbP54c//5rpy7ctCjk4l6vq4cus7U7Kvnr2btwrU8VPIQxVnFCS8PhvNr627jis9M7PnVT3t3\ne8KfryuHotDMPnbrY0zLmGZr1MOJ8mA4v5xJOf3DnWmShneSN+HPN26jQxEk5BUR2Wgd/0REftd2\nRCPHEjO/ROUO55c7OZe/+drfcPesu1k5d2XsXlRo02asucNhxEoQlJB3OVACfFNESoaYLQe+aG1/\nBLw82kASabLMSdwj+bX3tLOhfMM1j04m6vnGSw7dBhxT1UZVvQK8ATwwxOYB4B+sVay7gSwRieyp\nbQvuZFnyxJSMciiShLyjTdprG05sZuPNbdcv1bjDYUw7xsNlpXblUPLElIxyKJKEvBEl7VXV11S1\nTFXLcnJyBh1z5VDyxJSMcqgO+KKIFIlIOvAI8MshNr8EHrdGiZYCn6lqq+2ohoETm9l4c9v1SzXu\ncBixEqhqHxBIyHsI+LlaCXkDSXmB7UAjcAz4MfBfRxuIK4eSJ6ZklEOo6nZVnauqs1X1e1bZK2oy\nUmONCv2xdXy+qu4ZnvFauHIoeWJKRjnkKDixmY03t12/VOMOB8dUAlcOJU9MSSmHxgKuHEqemFw5\nFGM4sZmNN7ddv1TjDsvnlJQrtSdryZuSR3PnwHuM453jMhG4nRhTouUidUxLEDiBuhbzfuC6ljrK\n8sooyyvrL+vt6wUG3hkcysauX6JyOzGmseYOvn5sIdJ8jdHehuYibbzQqC98+II2Xmjs33/mvWf0\nmfee6S8bmncylI1dv0TldmJMY80dfN0EwChykTrm8cqWSy1UllT2ZxEryi5i0fRF/Z8BPGkenr3z\n2f4MxKFs7PolKrcTYxpr7sqSSloutQzKQDcaOKZP4MJFNDGaPkHcKoGItAEnYkTvBdpjxD0aOCUO\nSL1YZqlqzshmcawEsYSI7In0LpAKcYAby3BwzOiQCxfxglsJXKQ8krUSvBbvACw4JQ5wYwmLpOwT\nuHAxGiRrS+DCRcRI2EoQQUKw1VYisAMisktEFsQrliC7xSLSJyIPxTMWEfmyiHwsIvUiUhOvWERk\nqoi8LSL7rVh+P1axDItIp5adtAEe4DhQDKQD+4GSITZ3ANnW5+XAf8QrliC79zGPoj4Ux98lC2gA\nCqz9G+MYy/8AXrA+5wAXgPSxvp4StSUYMSGYqu5S1Q5rdzcmA0ZcYrHwbeAt4NMYxRFpLI8C/6Sq\nJwFUNVbxRBKLAlPErI2ejKkEfTGKJywStRKMNtnXWuDdeMUiIvnAg9hITxntWIC5QLaI/FpE9orI\n43GM5e+BeUALcAB4WlX9MYonLByzgC5WEJF7MZXgrjiG8X3gGVX1R/uBEBsYBywC7gMmAh+JyG5V\nPRKHWCqAj4GvALOB90TkA1W9OJZBJGoliCjZl4jcCvwEWK6q5+MYSxnwhlUBvMAKEelT1V/EIZbT\nwHlV7Qa6RWQnsACIdiWIJJbfB55X0yk4JiJNwC3Ab6Icy/AY605IlDpd4zB5jooY6HSVDrEpwORB\nuiPesQyx30zsOsaR/C7zgF9ZthnAQeBLcYrlZWCD9fkmTCXxjvX1lJAtgar2iUggIZgH2KRWQjDr\n+CvAd4FpwI+sO3CfxmDRVoSxjAkiiUVVD4nIDuATwA/8RFUPxiMW4H8Bm0XkACAYyTjmK13dGWMX\nKY9EHR1y4SJqcCuBi5SHWwlcpDzcSuAi5eFWAhcpD7cSuEh5uJXARcrDrQQuUh7/HxnDQf507zJ0\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x147257b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xfa8d240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xe5f5240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10586240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i,j in zip([1,3,5,11],[221,222,223,224]):\n",
    "    ada = train_adboost(dataset, num_base=i)\n",
    "    plot_boundary(dataset, ada, j, i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 另外，发现一个打印树的包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from IPython.display import Image  \n",
    "import pydotplus "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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RJ4RyX8ra39HBDpGH/0t0LSqQZcNmH5E5Qteu9OKLPpV13qJQdnCJPAE5p1LTMNXJRaqM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\naq6yzeh1d0pgauoodF4oerN///4w7tYF2bdr5Lr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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = base_learner.fit(dataset.ix[:,[0,1]], dataset.ix[:,2])\n",
    "dot_data = tree.export_graphviz(ada_boost.estimators_[0], out_file=None, \n",
    "                         filled=True, rounded=True,  \n",
    "                         special_characters=True)  \n",
    "graph = pydotplus.graph_from_dot_data(dot_data)  \n",
    "Image(graph.create_png()) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  },
  "toc": {
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": "block",
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
